Sentiment analysis of tweets using text and graph multi-views learning.
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| Title: | Sentiment analysis of tweets using text and graph multi-views learning. |
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| Authors: | Singh, Loitongbam Gyanendro1 (AUTHOR) gyanendro.loitongbam@soton.ac.uk, Singh, Sanasam Ranbir2 (AUTHOR) |
| Source: | Knowledge & Information Systems. May2024, Vol. 66 Issue 5, p2965-2985. 21p. |
| Subjects: | Deep learning, Microblogs, Sentiment analysis, Graph neural networks, Graph algorithms |
| Abstract: | With the surge of deep learning framework, various studies have attempted to address the challenges of sentiment analysis of tweets (data sparsity, under-specificity, noise, and multilingual content) through text and network-based representation learning approaches. However, limited studies on combining the benefits of textual and structural (graph) representations for sentiment analysis of tweets have been carried out. This study proposes a multi-view learning framework (end-to-end and ensemble-based) that leverages both text-based and graph-based representation learning approaches to enrich the tweet representation for sentiment classification. The efficacy of the proposed framework is evaluated over three datasets using suitable baseline counterparts. From various experimental studies, it is observed that combining both textual and structural views can achieve better performance of sentiment classification tasks than its counterparts. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | With the surge of deep learning framework, various studies have attempted to address the challenges of sentiment analysis of tweets (data sparsity, under-specificity, noise, and multilingual content) through text and network-based representation learning approaches. However, limited studies on combining the benefits of textual and structural (graph) representations for sentiment analysis of tweets have been carried out. This study proposes a multi-view learning framework (end-to-end and ensemble-based) that leverages both text-based and graph-based representation learning approaches to enrich the tweet representation for sentiment classification. The efficacy of the proposed framework is evaluated over three datasets using suitable baseline counterparts. From various experimental studies, it is observed that combining both textual and structural views can achieve better performance of sentiment classification tasks than its counterparts. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 02191377 |
| DOI: | 10.1007/s10115-023-02053-8 |
